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SurfaceScience21 – wissenschaftliches Programm

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O: Fachverband Oberflächenphysik

O 71: Mini-Symposium: Machine learning applications in surface science I

O 71.2: Vortrag

Mittwoch, 3. März 2021, 14:15–14:30, R1

Automated Tip Functionalization and Image interpretation with Machine Learning in Atomic Force MicroscopyBenjamin Alldritt1, Chen Xu1, Prokop Hapala3, Niko Oinonen2, •Fedor Urtev1, Lauri Kurki2, Ondrej Krejci1, Filippo Federici Canova1, Juho Kannala1, Peter Liljeroth1, and Adam Foster11Aalto University, Espoo, Finland — 2University of Helsinki, Finland — 3Czech Academy of Sciences, Prague, Czechia

Atomic force microscopy (AFM) is ubiquitous nanoscale characterisation technique to measure a 3D map of surface roughness at atomic resolutions [1]. AFM data interpretation and quantitative analysis for complex mixtures of molecules and bulky 3D molecules can be difficult [2], due to the complex nature of contrast in AFM images, and need significant acceleration and automation to make AFM technique available to a wide range of laboratories and clinics. Here, we introduce a machine learning (ML) approach both for the preparation of AFM experiments and for data interpretation in AFM. For the first objective our method involves a convolutional neural network (CNN) that has been trained to analyse the quality of a CO-terminated tip. For the interpretation of AFM images, we introduce ML image descriptors characterising the molecular configuration, allowing us to predict the molecular structure directly. [1] L. Gross et al., Science, vol. 325, no. 5944, (2009). [2] O. M. Gordon and P. J. Moriarty, Mach. Learn. Sci. Technol., vol. 1, no. 2, (2020).

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